CN112946743B - Method for distinguishing reservoir types - Google Patents
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Abstract
The invention discloses a method for distinguishing reservoir types, which comprises the following steps: s1, calculating a porosity and crack density data plane graph of a target interval, and carrying out normalization processing on the porosity and the crack density of each calculation grid point in the plane graph to obtain attribute data of the normalized porosity and crack density; s2, performing vector calculation on the two vectors on each calculation grid point by using the two attribute data on the calculation grid point and setting the two vectors of each grid point on the plane to obtain a reconstructed vector attribute plan for distinguishing reservoir types and the like; the invention can accurately distinguish the distribution states of different reservoir types and development intensities of geological abnormal bodies such as a river channel system on a plane.
Description
Technical Field
The invention relates to the technical field of seismic data interpretation in geophysical exploration, in particular to a method for distinguishing reservoir types.
Background
In practical oil and gas exploration, reservoirs are found to have certain physical characteristics, and the types of reservoirs also have various physical characteristics. It is also possible to develop multiple types of reservoirs within a single investigation region, such as fractured or open pore reservoirs, and some may be fractured-open pore or even karst. Therefore, it is more common to develop multiple types of reservoirs within a single research area (e.g., land-phase river reservoirs). In general, these different types of reservoirs have multiple physical manifestations that can be predicted by different geophysical techniques. Physical properties such as fracture and karst can be described by geophysical techniques such as coherence, curvature and P-wave anisotropy; the porosity can be obtained by using inversion data such as wave impedance and the like to correspond to the inversion data and performing correlation calculation.
Conventional reservoir prediction techniques are widely varied and can be largely divided into two major categories, pre-stack and post-stack inversion. However, when multiple types of reservoirs exist in a research area, often, the single inversion result cannot distinguish and describe the multiple types of reservoirs well. The invention patent No. CN200910243754.9, namely a method for predicting a good-quality reservoir of an oil reservoir by utilizing low-frequency seismic attributes, utilizes the frequency spectrum characteristics of seismic data, performs frequency division processing and amplitude spectrum gradient data volume calculation on the seismic data of a target interval, and then performs denoising and imaging processing on the amplitude spectrum gradient data volume to form an amplitude spectrum gradient data volume which is finally applied to the prediction of the good-quality reservoir, so as to compile a plan view of the good-quality reservoir. In recent years, many technical methods for predicting various physical properties of reservoirs have been developed, and research is relatively mature. However, the comprehensive prediction techniques for various types of reservoirs are not yet perfect, and mainly manifest in the following aspects:
(1) Conventional reservoir prediction techniques are typically used to predict only a comprehensive type or a single type of response of a reservoir, and are not capable of predicting and distinguishing multiple types of reservoirs.
(2) Conventional reservoir plans do not have comprehensive information of porosity and fracture density, but rather are a relative type of prediction.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for distinguishing reservoir types, which can accurately distinguish the distribution states of different reservoir types and development intensity of geological abnormal bodies such as a river channel system on a plane and the like.
The invention aims at realizing the following scheme:
a method of distinguishing reservoir types, comprising the steps of:
s1, calculating a porosity and crack density data plane graph of a target interval, and carrying out normalization processing on the porosity and the crack density of each calculation grid point in the plane graph to obtain attribute data of the normalized porosity and crack density;
s2, performing vector calculation on the two vectors on each calculation grid point by using the two attribute data on the calculation grid point and setting the two vectors of each grid point on the plane, and obtaining a reconstructed vector attribute plan for distinguishing reservoir types.
Further, in step S1, the steps of:
s11, performing calculation by utilizing seismic data aiming at a target interval to obtain a porosity and crack density data body;
s12, setting the positions of the calculated grid points on the plane by using the target horizon data and the normalized porosity and crack density data body, so as to extract the average value of the porosity and the crack density of each calculated grid point on the target horizon plane and obtain the normalized porosity and crack density data value of each calculated grid point on the plane.
Further, in step S11, the steps of:
s111, after the target interval is subjected to synthetic record calibration by utilizing the seismic data and the well data and the geological stratification, determining the reflection position of the target interval in the post-stack seismic data volume; tracking or explaining the top and bottom of a target layer section in the seismic data volume to obtain top and bottom horizon data of the target layer;
s112, calculating by using the porosity and crack density curve in the well and the seismic data, and processing to obtain a porosity and crack density data body;
and S113, carrying out normalization calculation on the porosity and crack density data body to obtain the normalized porosity and crack density data body.
Further, in step S2, the steps of:
s21, setting the normalized porosity and crack density vector of each calculation grid point on the plane;
s22, carrying out vector calculation on the porosity and crack density vectors on each calculation grid point on the plane to obtain a reconstructed vector attribute plane graph.
Further, the display mode of the reconstructed vector attribute plan view comprises setting display grid points to calculate reconstructed vector attribute values on the grid points for relevant plane contour line display; or, the reconstructed vector on the calculated grid points is subjected to superposition display on the contour line plane graphs of the two vector attributes, the color of the reconstructed vector is related to the gas content, and different types of reservoirs can be displayed by utilizing the contour line plane graphs of the two attribute data, so that the purpose of oil and gas exploration is achieved.
Further, the mesh setting display grid points is smaller than the calculation mesh.
Further, gridding calculation interpolation is carried out according to the set display grid points according to the vector data value and the vector azimuth angle on the calculated grid points, and then a contour line plan is generated.
Further, in step S112, the steps of:
and respectively calculating a plurality of attribute data bodies related to the porosity and the fracture density, respectively extracting attribute curves related to the porosity or the fracture density at well points, performing related operation on the attribute curves and the porosity and the fracture density curves after time-depth conversion into a time domain, selecting the attribute data body with the highest related coefficient for subsequent calculation, thus obtaining two attribute data bodies related to the calculation of the porosity and the fracture density, respectively extracting attribute data of a target interval on the well points, performing fitting calculation on the porosity and the fracture density data, obtaining related fitting calculation formulas, and converting the two attribute data bodies into the porosity and the fracture density data body by using the two fitting calculation formulas.
Further, in step S113, the normalization processing calculation formula is as follows:
in the above, x p To normalize the pre-processed fracture density or porosity data volume, x pi To normalize the processed fracture density or porosity attribute data volume, x max ＝max{x p }，x min ＝min{x p -a }; the fracture density or porosity attribute data volume is normalized to between 0 and k, k being a custom coefficient.
Further, in step S21, the steps of:
s211, setting a two-dimensional coordinate system, setting different and fixed vector azimuth angles for the vector directions of the porosity and the crack density, and setting the magnitude of a related vector value according to the normalized porosity and the crack density data value; in a set two-dimensional coordinate system, the set porosity and the vector azimuth angle of the crack density are not more than 90 degrees based on a symmetrical relation, wherein the set porosity and the crack density are rotated by 360 degrees in the clockwise direction with the north direction being 0 degrees, and the vector azimuth angles of the porosity and the crack density are fixed; the vector azimuth angle on the grid point is calculated and set as an included angle between the vector direction of the point and the north direction;
in step S22, the steps of:
s22, carrying out addition operation on the reconstruction vector on the plane calculation grid point according to the porosity and the fracture density vector on the grid point to obtain the reconstruction vector on the calculation grid point, and sequentially carrying out operation to obtain a reconstruction vector attribute plan for distinguishing reservoir types; the vector addition calculation formula is as follows:
in the above-mentioned method, the step of,for the reconstruction vector of a certain grid point, +.>For the porosity vector of this point, +.>A crack density vector for the point;
the calculation formula for calculating the size and direction of the reconstruction vector of a certain grid point is as follows:
K i ＝(X 2 i +Y 2 i ) 1/2 (3)
θ＝arctg(X i /Y i ) (4)
in the formulas (3) and (4), K i Calculating a reconstruction vector data value, X, at the grid point for the ith calculation i Setting an attribute vector data value on the X-axis for the calculated grid point on the two-dimensional coordinates, Y i And calculating an attribute vector data value of the grid point on the Y axis set on the two-dimensional coordinate, wherein θ is an included angle-vector azimuth angle between the reconstructed vector direction and the Y axis.
The beneficial effects of the invention are as follows:
the method accurately distinguishes the distribution states of different reservoir types and development intensities of geological abnormal bodies such as a river channel system on a plane and the like; specifically, the method predicts, distinguishes and evaluates different types of reservoirs (such as river channel systems and the like) by using vector operation, and mainly has different types of reservoirs-porosity, cracks and crack + porosity based on physical characteristics of the reservoirs with cracks and porosity. The normalized porosity and crack density data body is obtained by calculating the porosity and crack density data body and carrying out normalization calculation on the calculated porosity and crack density data body; extracting data of calculation grid points on the plane of the target layer of the two data volumes, setting fixed vector directions of the two attributes, carrying out assignment on vectors of related calculation grid points, and then carrying out addition operation to obtain reconstruction vectors of all calculation grid points; and analyzing different types of reservoirs in the research area by using the reconstructed vector plan, and delineating a development area of the high-quality reservoir. Because vector calculation is used, a clearer, visual and distinguishable plane distribution diagram of different types of reservoirs is obtained, and the geological effect is superior to that of a conventional single reservoir prediction plane diagram. The invention has good effect on detecting the sea reef beach reservoir, shale gas reservoir, sandstone reservoir in river channel and the like of the Sichuan basin, can distinguish reservoirs of different types on a plane, and has higher coincidence degree with real drilling data in the implementation area.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a technical route of the present invention;
fig. 2 is a schematic diagram of vector-related parameters of a grid point according to an embodiment of the present invention;
FIG. 3 is a schematic view of a reconstruction vector of a grid point according to an embodiment of the present invention;
fig. 4 is a flow chart of the steps of the method of the present invention.
Detailed Description
All of the features disclosed in all of the embodiments of this specification, or all of the steps in any method or process disclosed implicitly, except for the mutually exclusive features and/or steps, may be combined and/or expanded and substituted in any way.
Aiming at the defects of the conventional reservoir prediction technology in the background technology, the scheme provided by the invention is utilized to predict the multiple reservoir types in the research area so as to conveniently know the plane distribution condition of the different reservoir types, thereby better serving for oil and gas exploration.
As shown in fig. 1-4, a method of distinguishing reservoir types includes the steps of:
s1, calculating a porosity and crack density data plane graph of a target interval, and carrying out normalization processing on the porosity and the crack density of each calculation grid point in the plane graph to obtain attribute data of the normalized porosity and crack density;
s2, performing vector calculation on the two vectors on each calculation grid point by using the two attribute data on the calculation grid point and setting the two vectors of each grid point on the plane, and obtaining a reconstructed vector attribute plan for distinguishing reservoir types.
Further, in step S1, the steps of:
s11, performing calculation by utilizing seismic data aiming at a target interval to obtain a porosity and crack density data body;
s12, setting the positions of the calculated grid points on the plane by using the target horizon data and the normalized porosity and crack density data body, so as to extract the average value of the porosity and the crack density of each calculated grid point on the target horizon plane and obtain the normalized porosity and crack density data value of each calculated grid point on the plane. The calculation grid point may be set to m-line Xn trace, and in the case of the general embodiment, may be set to 20-line X20 trace.
Further, in step S11, inversion or attribute calculation may be performed on the target interval using the related pre-stack or post-stack seismic data to obtain a porosity and fracture density data volume, including the steps of:
s111, after the target interval is subjected to synthetic record calibration by utilizing the seismic data and the well data and the geological stratification, determining the reflection position of the target interval in the post-stack seismic data volume; tracking or explaining (can automatically track and manually explain) the top and bottom of a target layer section in the seismic data volume to obtain top and bottom layer position data of the target layer;
s112, calculating by using the porosity and crack density curve in the well and the seismic data, and processing to obtain a porosity and crack density data body;
and S113, carrying out normalization calculation on the porosity and crack density data body to obtain the normalized porosity and crack density data body.
Further, in step S2, the steps of:
s21, setting the normalized porosity and crack density vector of each calculation grid point on the plane;
s22, carrying out vector calculation on the porosity and crack density vectors on each calculation grid point on the plane to obtain a reconstructed vector attribute plane graph.
Further, the display mode of the reconstructed vector attribute plan view comprises setting display grid points to calculate reconstructed vector attribute values on the grid points for relevant plane contour line display; or, the reconstructed vector on the calculated grid points is subjected to superposition display on the contour line plane graphs of the two vector attributes, the color of the reconstructed vector is related to the gas content, and different types of reservoirs can be displayed by utilizing the contour line plane graphs of the two attribute data, so that the purpose of oil and gas exploration is achieved.
The display mode of the reconstructed vector attribute plan view can be that grid points are set to be displayed, so that the reconstructed vector attribute values on the grid points are calculated to display relevant plane contour lines. The reconstructed vector attribute value comprises a vector data value and a vector azimuth angle, so that two vector attribute contour line plane diagrams are displayed. In addition, the two vector attribute contour plane graphs may be superimposed and displayed by a vector bar on the calculation grid point, and the color of the vector bar may be correlated with the gas content. The two attribute data contour line plane diagrams can be used for displaying different types of reservoirs, so that the purpose of oil and gas exploration is achieved.
Further, the mesh setting display grid points is smaller than the calculation mesh. If the calculated grid point is 20 lines X20 lanes, the display grid point is set to 10 lines X10 lanes.
Further, gridding calculation interpolation is carried out according to the set display grid points according to the vector data value and the vector azimuth angle on the calculated grid points, and then a contour line plan is generated. Common methods for computer contour drawing are a triangle mesh method and a grid method.
The embodiment of the color of the reconstruction vector related to the gas content is to implement calculation of the gas content data of the calculation grid and display the gas content of the calculation grid by performing color display on the calculation grid. In principle, the reconstruction vector can be designed such that a warm tone indicates a high gas content and a cool tone indicates a poor gas content. In practice, for example, the absorption-attenuation attribute may be used as a gas-containing analysis attribute of the reservoir, and the absorption-attenuation attribute at the grid point may be calculated on the extraction plane thereof as a reconstruction vector color display.
Further, in step S112, the steps of:
and respectively calculating a plurality of attribute data bodies related to the porosity and the fracture density, respectively extracting attribute curves related to the porosity or the fracture density at well points, performing related operation on the attribute curves and the porosity and the fracture density curves after time-depth conversion into a time domain, selecting the attribute data body with the highest related coefficient for subsequent calculation, thus obtaining two attribute data bodies related to the calculation of the porosity and the fracture density, respectively extracting attribute data of a target interval on the well points, performing fitting calculation on the porosity and the fracture density data, obtaining related fitting calculation formulas, and converting the two attribute data bodies into the porosity and the fracture density data body by using the two fitting calculation formulas. In general, there are well-established calculation methods for porosity and fracture density calculation that can be implemented, and are not specifically described in the present invention.
Further, in step S113, the normalization processing calculation formula is as follows:
in the above, x p To normalize the pre-processed fracture density or porosity data volume, x pi To normalize the processed fracture density or porosity attribute data volume, x max ＝max{x p }，x min ＝min{x p -a }; the fracture density or porosity attribute data volume is normalized to between 0 and k, k being a custom coefficient. In an embodiment, x is further taken min =0, k=1, i.e. the data values of the three first attribute data volumes are normalized to the 0-1 value range.
Further, in step S21, the steps of:
s211, setting a two-dimensional coordinate system, setting different and fixed vector azimuth angles for the vector directions of the porosity and the crack density, and setting the magnitude of a related vector value according to the normalized porosity and the crack density data value; in a set two-dimensional coordinate system, the set porosity and the vector azimuth angle of the crack density are not more than 90 degrees based on a symmetrical relation, wherein the set porosity and the crack density are rotated by 360 degrees in the clockwise direction with the north direction being 0 degrees, and the vector azimuth angles of the porosity and the crack density are fixed; the vector azimuth angle on the grid point is calculated and set as an included angle between the vector direction of the point and the north direction;
in step S22, the steps of:
s22, carrying out addition operation on the reconstruction vector on the plane calculation grid point according to the porosity and the fracture density vector on the grid point to obtain the reconstruction vector on the calculation grid point, and sequentially carrying out operation to obtain a reconstruction vector attribute plan for distinguishing reservoir types; the vector addition calculation formula is as follows:
in the above-mentioned method, the step of,for the reconstruction vector of a certain grid point, +.>For the porosity vector of this point, +.>A crack density vector for the point;
the calculation formula for calculating the size and direction of the reconstruction vector of a certain grid point is as follows:
K i ＝(X 2 i +Y 2 i ) 1/2 (3)
θ＝arctg(X i /Y i ) (4)
in the formulas (3) and (4), K i Calculating a reconstruction vector data value, X, at the grid point for the ith calculation i Setting an attribute vector data value on the X-axis for the calculated grid point on the two-dimensional coordinates, Y i And calculating an attribute vector data value of the grid point on the Y axis set on the two-dimensional coordinate, wherein θ is an included angle-vector azimuth angle between the reconstructed vector direction and the Y axis.
In other embodiments of the present invention, the following technical solutions may be implemented, where the technical flow is shown in fig. 1, and the specific steps are as follows:
(1) and calculating a porosity and crack density data plane graph of the target interval, and normalizing the porosity and crack density of each calculation grid point in the plane graph to obtain attribute data of the normalized porosity and crack density.
(2) And performing vector calculation on the two vectors on each calculation grid point by using the two attribute data on the calculation grid point and setting the two vectors of each grid point on the plane to obtain a reconstructed vector attribute plane graph for distinguishing reservoir types.
The method comprises the steps of calculating a porosity and crack density data plane graph of a target interval, normalizing the porosity and crack density of each calculated grid point in the plane graph to obtain attribute data of the normalized porosity and crack density, and comprises the following steps:
and (i) performing inversion or attribute calculation by utilizing related pre-stack or post-stack seismic data aiming at the target interval to obtain a porosity and fracture density data body. The implementation method comprises the following specific steps:
(S1-a) determining the reflection position of the target interval in the post-stack seismic data body after the target interval is subjected to synthetic record calibration by utilizing the seismic data, well data, geological stratification and the like. And automatically tracking or manually explaining the top and bottom of the target layer section in the seismic data volume to obtain the top and bottom layer position data of the target layer.
(S1-b) inversion or attribute calculation and the like are carried out by using the porosity and fracture density curve in the well and pre-stack or post-stack seismic data, and a porosity and fracture density data body is obtained through correlation processing. In actual operation, a plurality of attribute data volumes related to the porosity and the fracture density are calculated respectively, attribute curves related to the porosity or the fracture density at well points are extracted respectively, and are related to the porosity and the fracture density curves after time-lapse deep conversion into a time domain for carrying out related operation, and the attribute data volume with the highest related coefficient is optimized for carrying out subsequent calculation. And respectively extracting the attribute data of the target interval on the well point and the porosity and the fracture density data to carry out fitting calculation to obtain a relevant fitting calculation formula, and converting the two attribute data into the porosity and the fracture density data by using the two fitting calculation formulas. The above operations are merely illustrative of one method of calculating porosity and fracture density. In general, there are well-established calculation methods for porosity and fracture density calculation that can be implemented, and are not specifically described in the present invention. In the step (S1-b) of the present invention, the correlation coefficient calculation formula is:
in the above, X i Y and Y i To perform the i-th data value of the two data of the correlation calculation,is->The values of r are respectively the average value of the rank ordering of the two data values, and the range of the values of r is 0 to 1.
And (S1-c) carrying out normalization calculation on the porosity and crack density data body to obtain the normalized porosity and crack density data body. The normalization process calculation formula is as follows:
in the above, x p To normalize the pre-processed fracture density or porosity data volume, x pi To normalize the processed fracture density or porosity attribute data volume, x max ＝max{x p }，x min ＝min{x p }. The fracture density or porosity attribute data volume may be normalized to between 0 and k, in this embodiment taking x min =0, k=1, i.e. the data values of the three first attribute data volumes are normalized to the 0-1 value range.
And ii) setting the positions of the calculated grid points on the plane by using the target horizon data and the normalized porosity and crack density data body, so as to extract the average value of the porosity and the crack density of each calculated grid point on the target horizon plane and obtain the normalized porosity and crack density data value of each calculated grid point on the plane. The calculation grid point may be set to m-line Xn trace, and may be set to 20-line X20 trace in general.
The method comprises the following steps of:
and i, setting the normalized porosity and crack density vector of each calculated grid point on the plane. The two-dimensional coordinate system is set, the vector directions of the porosity and the crack density are set to different and fixed vector azimuth angles, and the magnitude of the related vector values is set according to the normalized porosity and the crack density data values. In the two-dimensional coordinate system, the rotation in the clockwise direction is 360 ° with the north direction being 0 °. Based on the symmetry, the set vector azimuth angle of the porosity and the fracture density is basically not larger than 90 degrees, and the vector azimuth angles of the porosity and the fracture density are fixed. Wherein the vector azimuth on the calculation grid point is set as the angle (clockwise rotation) between the vector direction of the point and the north direction.
And ii, vector calculation is carried out on the porosity and crack density vectors on all calculation grid points on the plane, and a reconstructed vector attribute plane graph is obtained. In principle, the reconstruction vector on the calculation grid point on the plane is mainly obtained by carrying out addition operation according to the porosity and fracture density vector on the grid point, so as to obtain the reconstruction vector on the calculation grid point, namely a vector bar, and the reconstruction vector attribute plane graph for distinguishing the reservoir types is obtained by carrying out sequential operation, wherein the vector addition calculation formula is as follows:
in the above-mentioned method, the step of,reconstruction vector (or vector bar) for a certain grid point, for>For the porosity vector of this point, +.>Is the crack density vector for that point.
The calculation formula for calculating the size and direction of the reconstruction vector of a certain grid point is as follows:
K i ＝(X 2 i +Y 2 i ) 1/2
θ＝arctg(X i /Y i )
in the above, K i Calculating a reconstruction vector data value, X, at the grid point for the ith calculation i Setting an attribute vector data value on the X-axis for the calculated grid point on the two-dimensional coordinates, Y i And calculating an attribute vector data value of the grid point on the Y axis set on the two-dimensional coordinate, wherein θ is an included angle-vector azimuth angle between the reconstructed vector direction and the Y axis.
Iii, the display mode of the reconstructed vector attribute plan view can be that grid points are set to be displayed, and the reconstructed vector attribute values on the grid points are calculated to display relevant plane contour lines. The reconstructed vector attribute value comprises a vector data value and a vector azimuth angle, so that two vector attribute contour line plane diagrams are displayed. In addition, the two vector attribute contour plane graphs may be superimposed and displayed by a vector bar on the calculation grid point, and the color of the vector bar may be correlated with the gas content. The two attribute data contour line plane diagrams can be used for displaying different types of reservoirs, so that the purpose of oil and gas exploration is achieved.
Preferably, the grid of the display grid point is set to be smaller than the calculation grid, and if the calculation grid point is 20 lines X20 lanes, the display grid point is set to 10 lines X10 lanes.
Preferably, the contour line plan is generated after gridding calculation interpolation is carried out according to the vector data value and the vector azimuth angle on the calculated grid points and the set display grid points respectively, and the common methods for drawing the contour line by a computer are a triangle mesh method and a grid mesh method.
Preferably, the embodiment of the vector bar whose color is related to the gas content is to perform calculation of its gas content data on the calculation grid and to color display its gas content. In principle, the vector bar may be designed such that a warm tone indicates a high gas content and a cool tone indicates a poor gas content. In practice, for example, the absorption-attenuation attribute may be used as a gas-containing analysis attribute of the reservoir, and the absorption-attenuation attribute at the grid point may be calculated on the extraction plane thereof as a vector bar color display.
In other embodiments of the present invention, different types of reservoir predictions and evaluations are performed on the land-phase river system of a three-dimensional work area according to the steps shown in FIG. 1.
In the step (1), the reservoir sections of the land-phase river channel in the research area are mainly distributed in the two sections, the gas content is relatively good, and if micro cracks develop in the high-porosity reservoir, the micro cracks are favorable for fracturing, so that the high-quality reservoir is formed. Thus, according to step (1), a plurality of attribute data volumes related to the porosity and fracture density in the sandstone in the river are calculated. And (3) performing correlation operation by using the porosity curve and the fracture density curve of the sand and rock target layer in the well and the properties at the well points of each related property body, and performing fitting formula calculation according to the result of the correlation coefficient, preferably using the longitudinal wave impedance and P wave anisotropy intensity property data body to obtain the porosity and fracture density data body. The fitting calculation formula is obtained by carrying out intersection analysis on measured porosity or fracture density values on well points and well attribute values of the longitudinal wave impedance and P wave anisotropy intensity attribute data body respectively, and carrying out least square fitting on data points in an intersection graph to obtain a related calculation formula. In the embodiment, the two porosities and the fracture density data are normalized and calculated to be in the value range of 0 to 1. And according to the well earthquake calibration result, the top and bottom layer positions of the target interval are interpreted, and the horizon data of 1 line X1 channel are interpolated. And extracting average porosity and crack density values on each calculated grid point on the plane of the target interval by using the horizon data of the target interval and the normalized two attribute data volumes. Wherein, the grid of the calculation grid points is set as 10 lines X10 tracks.
In the step (2), vector assignment and addition operation are carried out according to the average porosity and crack density values on each calculation grid point on the plane of the target interval, so as to obtain a reconstruction vector (or vector bar) of each CDP point, and a reconstruction vector plan is obtained after correlation calculation. In the example, the vector direction of the porosity at the calculation grid point is set as the vector azimuth angle 0 DEG, the vector direction of the crack density at the calculation grid point is set as the vector azimuth angle 90 DEG, the two are included by 90 DEG, and the related vector data value is the normalized data value of the two (figures 2 and 3). And carrying out addition operation according to two vector values on each calculation grid point on the plane to obtain a reconstruction vector (vector bar) of each CDP point, obtaining reconstruction vector data (or vector bar) after correlation calculation, setting a display grid to be 10 lines X10 channels, and decomposing the reconstruction vector data into vector data and contour calculation to obtain a vector data contour plane graph and a vector azimuth contour plane graph. In the example, according to the distribution condition of the data points in the two vector contour line plane diagrams and related well data, the related different types of reservoir distribution conditions are obtained, for example, the data points in the range of 0-30 degrees of vector azimuth angle are microcrack + strong pore type reservoirs, and the pore type reservoirs are the main; the data points in the range of 30-60 degrees of vector azimuth angle are strong cracks and strong pore reservoirs, pores and cracks are relatively developed, and the data points are distribution areas of high-quality reservoirs; data points in the range of 60-90 degrees of vector azimuth angle are strong cracks and weak pore type reservoirs, and the fracture type reservoirs are the main. And projecting the relevant CDP points obtained by using the dividing result of the vector azimuth angles onto a vector data plane graph, and then evaluating the development intensity of the relevant reservoir layer on the vector data values of the CDP points. Generally, the larger the vector data value at a grid point, the more developed the porosity, fracture density, or both of the reservoir at that point are relative. The related results obtained by the invention are verified by the existing drilling data in the research area, and the anastomosis rate reaches 82.2%.
The distribution states of different types of reservoirs predicted by the technology of the invention are favorable for analyzing the distribution states of different types of reservoirs in the same research area because the distribution states integrate the related results of porosity and fracture density, and are superior to the attribute plan provided by the conventional single reservoir prediction technology. In addition, the technology of the invention can also be used for clearly implementing the detection of different types of reservoirs of the sea reef beach reservoir and the deep shale reservoir of the Sichuan basin, has good effect and has higher matching degree with the real drilling data of the relevant region.
The inventive functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium and executing all or part of the steps of the method according to the embodiments of the present invention in a computer device (which may be a personal computer, a server, or a network device, etc.) and corresponding software. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, and an optical disk, and test or actual data exist in a read-only memory (Random Access Memory, RAM), a random access memory (Random Access Memory, RAM), and the like in program implementation.
Claims (5)
1. A method of distinguishing reservoir types, comprising the steps of:
s1, calculating a porosity and crack density data plane graph of a target interval, and carrying out normalization processing on the porosity and the crack density of each calculation grid point in the plane graph to obtain attribute data of the normalized porosity and crack density;
in step S1, the steps include: s11, performing calculation by utilizing seismic data aiming at a target interval to obtain a porosity and crack density data body; s12, setting the positions of calculated grid points on a plane by using the target horizon data and the normalized porosity and crack density data body, so as to extract the average value of the porosity and the crack density of each calculated grid point on the target horizon plane and obtain the normalized porosity and crack density data value of each calculated grid point on the plane;
in step S11, the steps of: s111, after the target interval is subjected to synthetic record calibration by utilizing the seismic data and the well data and the geological stratification, determining the reflection position of the target interval in the post-stack seismic data volume; tracking or explaining the top and bottom of a target layer section in the seismic data volume to obtain top and bottom horizon data of the target layer; s112, calculating by using the porosity and crack density curve in the well and the seismic data, and processing to obtain a porosity and crack density data body; s113, carrying out normalization calculation on the porosity and crack density data body to obtain normalized porosity and crack density data body;
s2, performing vector calculation on the two vectors on each calculation grid point by using the two attribute data on the calculation grid point and setting the two vectors of each grid point on the plane to obtain a reconstructed vector attribute plan for distinguishing reservoir types;
in step S2, the steps of: s21, setting the normalized porosity and crack density vector of each calculation grid point on the plane; s22, carrying out vector calculation on the porosity and crack density vectors on each calculation grid point on the plane to obtain a reconstructed vector attribute plane graph;
the display mode of the reconstructed vector attribute plan comprises the steps of setting display grid points to calculate reconstructed vector attribute values on the grid points for relevant plane contour line display; or, overlapping and displaying the reconstructed vector on the calculated grid points on the two vector attribute contour line plane graphs, wherein the color of the reconstructed vector is related to the gas content, and different types of reservoirs can be displayed by utilizing the two attribute data contour line plane graphs, so that the purpose of oil and gas exploration is achieved;
in step S21, the steps of:
s211, setting a two-dimensional coordinate system, setting different and fixed vector azimuth angles for the vector directions of the porosity and the crack density, and setting the magnitude of a related vector value according to the normalized porosity and the crack density data value; in a set two-dimensional coordinate system, the set porosity and the vector azimuth angle of the crack density are not more than 90 degrees based on a symmetrical relation, wherein the set porosity and the crack density are rotated by 360 degrees in the clockwise direction with the north direction being 0 degrees, and the vector azimuth angles of the porosity and the crack density are fixed; the vector azimuth angle on the grid point is calculated and set as an included angle between the vector direction of the point and the north direction;
in step S22, the steps of:
s22, carrying out addition operation on the reconstruction vector on the plane calculation grid point according to the porosity and the fracture density vector on the grid point to obtain the reconstruction vector on the calculation grid point, and sequentially carrying out operation to obtain a reconstruction vector attribute plan for distinguishing reservoir types; the vector addition calculation formula is as follows:
in the above-mentioned method, the step of,for the reconstruction vector of a certain grid point, +.>For the porosity vector of this point, +.>A crack density vector for the point;
the calculation formula for calculating the size and direction of the grid point reconstruction vector is as follows:
K i ＝(X 2 i +Y 2 i ) 1/2 (3)
θ＝arctg(X i /Y i ) (4)
in the formulas (3) and (4), K i Calculating a reconstruction vector data value, X, at the grid point for the ith calculation i Setting an attribute vector data value on the X-axis for the calculated grid point on the two-dimensional coordinates, Y i And calculating an attribute vector data value of the grid point on the Y axis set on the two-dimensional coordinate, wherein θ is an included angle-vector azimuth angle between the reconstructed vector direction and the Y axis.
2. The method of discriminating reservoir types as defined in claim 1 wherein the grid of display grid points is set to be relatively smaller than the computational grid.
3. The method of claim 1, wherein the contour plan is generated by performing gridding calculation interpolation on the set display grid points according to the vector data values and the vector azimuth angles on the calculation grid points.
4. The method of discriminating reservoir types as defined in claim 1, including, in step S112, the steps of:
and respectively calculating a plurality of attribute data bodies related to the porosity and the fracture density, respectively extracting attribute curves related to the porosity or the fracture density at well points, performing related operation on the attribute curves and the porosity and the fracture density curves after time-depth conversion into a time domain, selecting the attribute data body with the highest related coefficient for subsequent calculation, thus obtaining two attribute data bodies related to the calculation of the porosity and the fracture density, respectively extracting attribute data of a target interval on the well points, performing fitting calculation on the porosity and the fracture density data, obtaining related fitting calculation formulas, and converting the two attribute data bodies into the porosity and the fracture density data body by using the two fitting calculation formulas.
5. The method of discriminating reservoir types as defined in claim 1, wherein in step S113, the normalization process calculates the formula as follows:
in the above, x p To normalize the pre-processed fracture density or porosity data volume, x pi To normalize the processed fracture density or porosity attribute data volume, x max ＝max{x p }，x min ＝min{x p -a }; the fracture density or porosity attribute data volume is normalized to between 0 and k, k being a custom coefficient.
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